Abstract
We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The findings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from fixed effect heterogeneity across individual station level observations. Difference GMM and Within Group (WG) estimation have little bias and WG estimation is recommended for practical implementation of dynamic panel regression with highly disaggregated climate data. Intriguingly, from an econometric perspective and importantly for global policy analysis, it is shown that in this model despite the substantial differences between the estimates of the regression model parameters, estimates of global transient climate sensitivity (of temperature to a doubling of atmospheric CO2) are robust to the estimation method employed and to the specific nature of the trending mechanism in global temperature, radiation, and CO2.
Highlights
A natural and near universal condition in modeling climate is the use of an energy balance relationship that links average global temperature to average global downwelling radiation and greenhouse gas influences
We report below results of a small simulation exercise with panel Within Group (WG), diff-GMM (Difference GMM), and system GMM (sys-GMM) (System GMM) estimation of the parameters in the following panel ARX(1) model (Storelvmo et al 2016): Tit
Panel data econometric methods seem well suited to assess the impact on global temperature of rising greenhouse gas (GHG) concentrations in Earth’s atmosphere
Summary
A natural and near universal condition in modeling climate is the use of an energy balance relationship that links average global temperature to average global downwelling radiation and greenhouse gas influences. A second method of estimation of TCS is to conduct a simple single equation cointegrating regression to capture the long-run impact of atmospheric CO2 levels on global temperature This procedure was explored in Phillips et al (2020) and shown to allow for energy imbalance, so that sustained rises in atmospheric CO2 may impact station level temperature while continuing to influence rising global temperature, a situation that approximates prevailing climate conditions and accords with earlier empirical studies with aggregate data (Kaufmann et al 2011, 2013). Proofs are given in Appendix B and additional figures in Appendix A
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